Dynamic control and regulation of critical database resources using a virtual memory table interface

ABSTRACT

A computer-implemented apparatus, method, and article of manufacture provide the ability to manage a plurality of database systems. A domain contains the database systems, and a database in one of the systems has segmented global memory partitions. A virtual monitor partition provides logon access to the segmented global memory partitions in a form of a virtual database. Open application programming interfaces (API) enable logon access to the virtual monitor partition to access data in the virtual database. A multi-system regulator manages the domain and utilizes the open APIs to access data in the virtual data base.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending andcommonly-assigned applications:

U.S. Utility patent application Ser. No. 10/730,348, filed Dec. 8, 2003,by Douglas P. Brown, Anita Richards, Bhashyam Ramesh, Caroline M.Ballinger and Richard D. Glick, and entitled Administering the Workloadof a Database System Using Feedback, attorney's docket no. 11167;

U.S. Utility patent application Ser. No. 10/786,448, filed Feb. 25,2004, by Douglas P. Brown, Bhashyam Ramesh and Anita Richards, andentitled Guiding the Development of Workload Group DefinitionClassifications, attorneys' docket no. 11569;

U.S. Utility patent application Ser. No. 10/889,796, filed Jul. 13,2004, by Douglas P. Brown, Anita Richards, and Bhashyam Ramesh, andentitled Administering Workload Groups, attorneys' docket no. 11560;

U.S. Utility patent application Ser. No. 10/915,609, filed Aug. 10,2004, by Douglas P. Brown, Anita Richards, and Bhashyam Ramesh, andentitled Regulating the Workload of a Database System, attorneys' docketno. 11561;

U.S. Utility patent application Ser. No. 11/468,107, filed Aug. 29,2006, by Douglas P. Brown and Anita Richards, and entitled A System andMethod for Managing a Plurality of Database Systems, attorneys' docketno. 12162, which applications claims the benefit of U.S. ProvisionalPatent Application Ser. No. 60/715,815, filed Sep. 9, 2005, by DouglasP. Brown and Anita Richards, and entitled A System and Method forManaging a Plurality of Database Systems, attorneys' docket no. 12162;

U.S. Provisional Patent Application Ser. No. 60/877,977, filed on Dec.29, 2006, by Douglas P. Brown and Anita Richards, and entitled ManagingEvents in a Computing Environment, attorneys' docket no. 12363;

U.S. Utility patent application Ser. No. 11/716,889, filed on Mar. 12,2007, by Douglas P. Brown, Anita Richards, Mark Morris and Todd A.Walter, and entitled Virtual Regulator for Multi-Database Systems,attorneys' docket no. 12787, which application claims the benefit ofU.S. Provisional Patent Application Nos. 60/877,766, 60/877,767,60/877,768, and 60/877,823, all of which were filed Dec. 29, 2006;

U.S. Utility patent application Ser. No. 11/716,892, filed on Mar. 12,2007, by Douglas P. Brown, Scott Gnau and Mark Morris, and entitledParallel Virtual Optimization, attorneys' docket no. 12841, whichapplication claims the benefit of U.S. Provisional Patent ApplicationNos. 60/877,766, 60/877,767, 60/877,768, and 60/877,823, all of whichwere filed Dec. 29, 2006;

U.S. Utility patent application Ser. No. 11/716,880, filed on Mar. 12,2007, by Mark Morris, Anita Richards and Douglas P. Brown, and entitledWorkload Priority Influenced Data Temperature, attorneys' docket no.12788, which application claims the benefit of U.S. Provisional PatentApplication Nos. 60/877,766, 60/877,767, 60/877,768, and 60/877,823, allof which were filed Dec. 29, 2006;

U.S. Utility patent application Ser. No. 11/716,890, filed on Mar. 12,2007, by Mark Morris, Anita Richards and Douglas P. Brown, and entitledAutomated Block Size Management for Database Objects, attorneys' docketno. 12789, which application claims the benefit of U.S. ProvisionalPatent Application Nos. 60/877,766, 60/877,767, 60/877,768, and60/877,823, all of which were filed Dec. 29, 2006;

U.S. Utility patent application Ser. No. 11/803,248, filed on May 14,2007, by Anita Richards and Douglas P. Brown, and entitled State Matrixfor Workload Management Simplification, attorneys' docket no. 12892;

U.S. Utility patent application Ser. No. 11/811,496, filed on Jun. 11,2007, by Anita Richards and Douglas P. Brown, and entitled Arrival RateThrottles for Workload Management, attorneys' docket no. 12919;

U.S. Utility patent application Ser. No. 11/891,919, filed on Aug. 14,2007, by Douglas P. Brown, Pekka Kostamaa, Mark Morris, Bhashyam Ramesh,and Anita Richards, and entitled Dynamic Query Optimization BetweenSystems Based on System Conditions, attorneys' docket no. 12866;

U.S. Utility patent application Ser. No. ______, filed on the same dateherewith, by Douglas P. Brown, Scott E. Gnau, John Mark Morris andWilliam P. Ward, and entitled Dynamic Query and Step Routing BetweenSystems Tuned for Different Objectives, attorneys' docket no. 12862;

U.S. Utility patent application Ser. No. ______, filed on the same dateherewith, by Douglas P. Brown and Debra A. Galeazzi, and entitledClosed-Loop System Management Method and Process Capable of ManagingWorkloads in a Multi-System Database, attorneys' docket no. 12655;

U.S. Utility patent application Ser. No. ______, filed the same dateherewith, by Douglas P. Brown, John Mark Morris and Todd A. Walter, andentitled Virtual Data Maintenance, attorneys' docket no. 12856;

all of which applications are incorporated by reference herein.

BACKGROUND

Modern computing systems execute a variety of requests concurrently andoperate in a dynamic environment of cooperative systems, each comprisingof numerous hardware components subject to failure or degradation.

The need to regulate concurrent hardware and software “events” has ledto the development of a field which may be generically termed “WorkloadManagement.” For the purposes of this application, “events” comprise,but are not limited to, one or more signals, semaphores, periods oftime, hardware, software, business requirements, etc.

Workload management techniques focus on managing or regulating amultitude of individual yet concurrent requests in a computing system byeffectively controlling resource usage within the computing system.Resources may include any component of the computing system, such as CPU(central processing unit) usage, hard disk or other storage means usage,or I/O (input/output) usage.

Workload management techniques fall short of implementing a full systemregulation, as they do not manage unforeseen impacts, such as unplannedsituations (e.g., a request volume surge, the exhaustion of sharedresources, or external conditions like component outages) or evenplanned situations (e.g., systems maintenance or data load).

Many different types of system conditions or events can impactnegatively the performance of requests currently executing on a computersystem. These events can remain undetected for a prolonged period oftime, causing a compounding negative effect on requests executing duringthat interval. When problematic events are detected, sometimes in an adhoc and manual fashion, the computing system administrator may still notbe able to take an appropriate course of action, and may either delaycorrective action, act incorrectly or not act at all.

A typical impact of not managing for system conditions is to deliverinconsistent response times to users. For example, often systems executein an environment of very cyclical usage over the course of any day,week, or other business cycle. If a user ran a report near standalone ona Wednesday afternoon, she may expect that same performance with manyconcurrent users on a Monday morning. However, based on the laws oflinear systems performance, a request simply cannot deliver the sameresponse time when running stand-alone as when it runs competing withhigh volumes of concurrency.

Therefore, while rule-based workload management can be effective in acontrolled environment without external impacts, it fails to respondeffectively when those external impacts are present.

In addition, currently there is no effective way to access criticalresources (for example, memory segments) in real-time such as DatabaseSystem (DBS) global memory partitions (virtual processors[Vproc]—Partition Global), performance memory partition data (PMPC) ortask partition (TskGlobal) data using SQL (structured query languagecommands such as select, update, delete) within the Teradata™ databasearchitecture. Access to critical resource data is key to successfullymanaging a database system. However, accessing this data is difficultwithout using invasive tools and methods such as: PMPC, GDB (GNUDebugger), KDB (built-in Kernel Debugger), SDB (symbolic debugger),Crash, Coroner, Puma, Trace, etc. Methods such as these can beprohibitively expensive and intrusive to customers because it requireslarge machine resources (CPU [central processing unit], traces, etc) orrequires the halting of tasks and/or threads which can stop theexecution of the database.

In addition to this problem, there currently is no way to debug externalroutines such as SQL Stored Procedures (SPL), UDFs (user definedfunctions) or LOBs (large objects), CLOBS (character large objects),and/or BLOBS (binary large objects).

Ideally a database management system (DBMS) should be able to acceptperformance goals for a workload and automatically adjust its ownperformance “knobs” using the goals as a guide. Given performanceobjectives for each workload, the problem is further complicated by thefact that workloads can interfere with each other's performance throughcompetition for shared system resources. Because of this interference,the DBMS may find a “knob” performance setting that achieves the goalfor one workload but at the same time makes it impossible to achieve thegoal for some other workloads. Further compounding the problem is thefact that system resources are of a finite number, with a limited numberavailable to perform work on the system.

Accordingly, what is needed is the ability to manage critical resourcesin real-time to allow features such as Teradata Active System Management(TASM) the ability to dynamically manage database (DBMS) resources suchas sessions, tasks, queues, access to CPU, I/O, etc. With thiscapability Teradata Active System Management can greatly improveTeradata's system management capabilities, with a focus on being able todynamically manage the database system.

In addition, allowing access to critical database resources will allowDBAs, engineers, and third party tools, the ability to monitor andmanage database machines using a different mechanism.

Accordingly, what is needed is the capability to manage and accesscritical database resources, in real time, in a multi-systemenvironment.

SUMMARY

Currently there is no effective way to manage critical databaseresources on a multisystem database through SQL. One or more embodimentsof the invention provide a computer-implemented method, apparatus, andarticle of manufacture that allows an application or user to dynamicallymanage a database system through use of a new virtual monitor tableinterface.

A computer-implemented method, apparatus, and article of manufactureprovide the ability to manage a plurality of database systems. A domainconsists of a plurality of database systems with a database in theplurality of database systems having segmented global memory partitions.A virtual monitor partition provides logon access to the segmentedglobal memory partitions in a form of a virtual database. One or moreopen application programming interfaces (API) are configured to logon tothe virtual monitor partition to access data in the virtual database. Amulti-system regulator manages the domain and is configured to utilizethe open APIs to access data in the virtual database.

The open APIs may be written as either external stored procedures (XSP)or user defined functions (UDFs). If the open API is an XSP, theprocedure may accept an extensible markup language (XML) file thatdefines fields to be selected from the segmented global memorypartitions. If the open API is written as a UDF, the UDF may access theinformation from the segmented global memory partition as thoughselecting from a virtual table. In addition, the open APIs may logon tothe virtual monitor partition using a command line interface.

Thus, a set of open application programming interfaces (APIs) enable aregulator and/or third party tools to perform various system resourcemanagement tasks. For example, critical system resource settings(throttles, filters, PSF weights, etc) can be regulated against workloadexpectations (SLG's) across workloads, system conditions can bemonitored and managed, response time requirements can be adjusted orregulated by workload, and PSF (priority scheduler facility) settingscan be dynamically modified to handle dynamic allocation of resourceweights within partitions so as to meet SLGs across systems. Inaddition, an alert can be raised to the database administrator who canbe permitted to post a message to a queue table (e.g., defer or executequery, recommendation, etc.).

The open APIs also provide the ability to cross-compare workloadresponse time histories (via a query log) with workload SLGs versussystem conditions to determine if query gating (flow control) should bealtered. For example, dynamic throttle and filter adjustments canprovide a real-time flow control mechanism. Critical system resourcescan be monitored and managed such as: throughput, arrival rates, AWTactivity, message queues, FSG (file segment) cache, memory usage, CPU,I/O, etc through SQL. Also, global support center (GSC) and supportengineers can be allowed to debug and analyze system problems via SQLscripts.

Other features and advantages will become apparent from the descriptionand claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a node of a database system.

FIG. 2 is a block diagram of a parsing engine.

FIG. 3 is a flow chart of a parser.

FIGS. 4-7 are block diagrams of a system for administering the workloadof a database system.

FIG. 8 is a flow chart of an event categorization and management system.

FIG. 9 illustrates how conditions and events may be comprised ofindividual conditions or events and condition or event combinations.

FIG. 10 is a table depicting an example rule set and working value set.

FIG. 11 illustrates an n-dimensional matrix that is used to perform withautomated workload management.

FIG. 12 illustrates a multi-system environment including a domain-levelvirtual regulator and a plurality of system-level regulators.

FIG. 13 is a flow chart illustrating a method for performing datamaintenance tasks in a data warehouse system in accordance with one ormore embodiments of the invention.

FIG. 14 illustrates how Open API functions interface through commandline interfaces and a monitor partition to interact with a databasesystem and access data in accordance with one or more embodiments of theinvention.

FIG. 15 illustrates the design of an external stored procedure inaccordance with one or more embodiments of the invention.

FIG. 16 illustrates a design for utilizing open APIs in accordance withone or more embodiments of the invention.

DETAILED DESCRIPTION

The event management technique disclosed herein has particularapplication to large databases that might contain many millions orbillions of records managed by a database system (“DBS”) 100, such as aTeradata Active Data Warehouse (ADW) available from NCR Corporation.FIG. 1 shows a sample architecture for one node 105 ₁ of the DBS 100.The DBS node 105 ₁ includes one or more processing modules 110 ₁ . . ._(N), connected by a network 115 that manage the storage and retrievalof data in data storage facilities 120 ₁ . . . _(N). Each of theprocessing modules 110 ₁ . . . _(N) may be one or more physicalprocessors or each may be a virtual processor, with one or more virtualprocessors running on one or more physical processors.

For the case in which one or more virtual processors are running on asingle physical processor, the single physical processor swaps betweenthe set of N virtual processors. Each virtual processor is generallytermed an Access Module Processor (AMP) in the Teradata Active DataWarehousing System.

For the case in which N virtual processors are running on an M processornode, the node's operating system schedules the N virtual processors torun on its set of M physical processors. If there are 4 virtualprocessors and 4 physical processors, then typically each virtualprocessor would run on its own physical processor. If there are 8virtual processors and 4 physical processors, the operating system wouldschedule the 8 virtual processors against the 4 physical processors, inwhich case swapping of the virtual processors would occur.

Each of the processing modules 110 ₁ . . . _(N) manages a portion of adatabase that is stored in a corresponding one of the data storagefacilities 120 ₁ . . . _(N). Each of the data storage facilities 120 ₁ .. . _(N) includes one or more disk drives. The DBS 100 may includemultiple nodes 105 ₂ . . . _(N) in addition to the illustrated node 105₁, connected by extending the network 115.

The system stores data in one or more tables in the data storagefacilities 120 ₁ . . . _(N). The rows 125 ₁ . . . _(z) of the tables arestored across multiple data storage facilities 120 ₁ . . . _(N) toensure that the system workload is distributed evenly across theprocessing modules 110 ₁ . . . _(N). A Parsing Engine (PE) 130 organizesthe storage of data and the distribution of table rows 125 ₁ . . . _(z)among the processing modules 110 ₁ . . . _(N). The PE 130 alsocoordinates the retrieval of data from the data storage facilities 120 ₁. . . _(N) in response to queries received from a user at a mainframe135 or a client computer 140. The DBS 100 usually receives queries in astandard format, such as SQL.

In one example system, the PE 130 is made up of three components: asession control 200, a parser 205, and a dispatcher 210, as shown inFIG. 2. The session control 200 provides the logon and logoff function.It accepts a request for authorization to access the database, verifiesit, and then either allows or disallows the access.

Once the session control 200 allows a session to begin, a user maysubmit a SQL request that is routed to the parser 205. As illustrated inFIG. 3, the parser 205 interprets the SQL request (block 300), checks itfor proper SQL syntax (block 305), evaluates it semantically (block310), and consults a data dictionary to ensure that all of the objectsspecified in the SQL request actually exist and that the user has theauthority to perform the request (block 315). Finally, the parser 205runs an optimizer (block 320) that develops the least expensive plan toperform the request.

The DBS 100 described herein accepts performance goals for each workloadas inputs, and dynamically adjusts its own performance, such as byallocating DBS 100 resources and throttling back incoming work. In oneexample system, the performance parameters are called priority schedulerparameters. When the priority scheduler is adjusted, weights assigned toresource partitions and allocation groups are changed. Adjusting howthese weights are assigned modifies the way access to the CPU, disk andmemory is allocated among requests. Given performance objectives foreach workload and the fact that the workloads may interfere with eachother's performance through competition for shared resources, the DBS100 may find a performance setting that achieves one workload's goal butmakes it difficult to achieve another workload's goal.

The performance goals for each workload will vary widely as well, andmay or may not be related to their resource demands. For example, twoworkloads that execute the same application and DBS 100 code could havediffering performance goals simply because they were submitted fromdifferent departments in an organization. Conversely, even though twoworkloads have similar performance objectives, they may have verydifferent resource demands.

The system includes a “closed-loop” workload management architecturecapable of satisfying a set of workload-specific goals. In other words,the system is a goal-oriented workload management system capable ofsupporting complex workloads and capable of self-adjusting to varioustypes of workloads. In Teradata, the workload management system isgenerally referred to as Teradata Active System Management (TASM).

The system's operation has four major phases: 1) assigning a set ofincoming request characteristics to workload groups, assigning theworkload groups to priority classes, and assigning goals (called ServiceLevel Goals or SLGs) to the workload groups; 2) monitoring the executionof the workload groups against their goals; 3) regulating (adjusting andmanaging) the workload flow and priorities to achieve the SLGs; and 4)correlating the results of the workload and taking action to improveperformance. The performance improvement can be accomplished in severalways: 1) through performance tuning recommendations such as the creationor change in index definitions or other supplements to table data, or torecollect statistics, or other performance tuning actions, 2) throughcapacity planning recommendations, for example increasing system power,3) through utilization of results to enable optimizer self-learning, and4) through recommending adjustments to SLGs of one workload to bettercomplement the SLGs of another workload that it might be impacting. Allrecommendations can either be enacted automatically, or after“consultation” with the database administrator (DBA).

The system includes the following components (illustrated in FIG. 4):

1) Administrator (block 405): This component provides a GUI to defineworkloads and their SLGs and other workload management requirements. Theadministrator 405 accesses data in logs 407 associated with the system,including a query log, and receives capacity planning and performancetuning inputs as discussed above. The administrator 405 is a primaryinterface for the DBA. The administrator also establishes workload rules409, which are accessed and used by other elements of the system.

2) Monitor (block 410): This component provides a top level dashboardview, and the ability to drill down to various details of workload groupperformance, such as aggregate execution time, execution time byrequest, aggregate resource consumption, resource consumption byrequest, etc. Such data is stored in the query log and other logs 407available to the monitor. The monitor also includes processes thatinitiate the performance improvement mechanisms listed above andprocesses that provide long term trend reporting, which may includingproviding performance improvement recommendations. Some of the monitorfunctionality may be performed by the regulator, which is described inthe next paragraph.

3) Regulator (block 415): This component dynamically adjusts systemsettings and/or projects performance issues and either alerts the DBA oruser to take action, for example, by communication through the monitor,which is capable of providing alerts, or through the exception log,providing a way for applications and their users to become aware of, andtake action on, regulator actions. Alternatively, the regulator 415 canautomatically take action by deferring requests or executing requestswith the appropriate priority to yield the best solution givenrequirements defined by the administrator (block 405). As described inmore detail below, the regulator 415 may also use a set of openapplication programming interfaces (APIs) to access and monitor globalmemory partitions.

The workload management administrator (block 405), or “administrator,”is responsible for determining (i.e., recommending) the appropriateapplication settings based on SLGs. Such activities as setting weights,managing active work tasks and changes to any and all options will beautomatic and taken out of the hands of the DBA. The user will be maskedfrom all complexity involved in setting up the priority scheduler, andbe freed to address the business issues around it.

As shown in FIG. 5, the workload management administrator (block 405)allows the DBA to establish workload rules, including SLGs, which arestored in a storage facility 409, accessible to the other components ofthe system. The DBA has access to a query log 505, which stores thesteps performed by the DBS 100 in executing a request along withdatabase statistics associated with the various steps, and an exceptionlog/queue 510, which contains records of the system's deviations fromthe SLGs established by the administrator. With these resources, the DBAcan examine past performance and establish SLGs that are reasonable inlight of the available system resources. In addition, the systemprovides a guide for creation of workload rules 515 which guides the DBAin establishing the workload rules 409. The guide accesses the query log505 and the exception log/queue 510 in providing its guidance to theDBA.

The administrator assists the DBA in: a) Establishing rules for dividingrequests into candidate workload groups, and creating workload groupdefinitions. Requests with similar characteristics (users, application,table, resource requirement, etc) are assigned to the same workloadgroup. The system supports the possibility of having more than oneworkload group with similar system response requirements. b) Refiningthe workload group definitions and defining SLGs for each workloadgroup. The system provides guidance to the DBA for response time and/orarrival rate threshold setting by summarizing response time and arrivalrate history per workload group definition versus resource utilizationlevels, which it extracts from the query log (from data stored by theregulator, as described below), allowing the DBA to know the currentresponse time and arrival rate patterns. The DBA can then cross-comparethose patterns to satisfaction levels or business requirements, ifknown, to derive an appropriate response time and arrival rate thresholdsetting, i.e., an appropriate SLG. After the administrator specifies theSLGs, the system automatically generates the appropriate resourceallocation settings, as described below. These SLG requirements aredistributed to the rest of the system as workload rules. c) Optionally,establishing priority classes and assigning workload groups to theclasses. Workload groups with similar performance requirements areassigned to the same class. d) Providing proactive feedback (i.e.,validation) to the DBA regarding the workload groups and their SLGassignments prior to execution to better assure that the currentassignments can be met, i.e., that the SLG assignments as defined andpotentially modified by the DBA represent realistic goals. The DBA hasthe option to refine workload group definitions and SLG assignments as aresult of that feedback.

The internal monitoring and regulating component (regulator 415),illustrated in more detail in FIGS. 6A and 6B, accomplishes itsobjective by dynamically monitoring the workload characteristics(defined by the administrator) using workload rules or other heuristicsbased on past and current performance of the system that guide twofeedback mechanisms. It does this before the request begins executionand at periodic intervals during query execution. Prior to queryexecution, an incoming request is examined to determine in whichworkload group it belongs, based on criteria as described in more detailbelow. Concurrency or arrival rate levels, i.e., the numbers ofconcurrent executing queries from each workload group, are monitored orthe rate at which they have been arriving, and if current workload grouplevels are above an administrator-defined threshold, a request in thatworkload group waits in a queue prior to execution until the levelsubsides below the defined threshold. Query execution requests currentlybeing executed are monitored to determine if they still meet thecriteria of belonging in a particular workload group by comparingrequest execution characteristics to a set of exception conditions. Ifthe result suggests that a request violates the rules associated with aworkload group, an action is taken to move the request to anotherworkload group or to abort it, and/or alert on or log the situation withpotential follow-up actions as a result of detecting the situation.Current response times and throughput of each workload group are alsomonitored dynamically to determine if they are meeting SLGs. A resourceweight allocation for each performance group can be automaticallyadjusted to better enable meeting SLGs using another set of heuristicsdescribed with respect to FIGS. 6A and 6B.

As shown in FIG. 6A, the regulator 415 receives one or more requests,each of which is assigned by an assignment process (block 605) to aworkload group and, optionally, a priority class, in accordance with theworkload rules 409. The assigned requests are passed to a workload query(delay) manager 610, which is described in more detail with respect toFIG. 7. The regulator 415 includes an exception monitor 615 fordetecting workload exceptions, which are recorded in a log 510.

In general, the workload query (delay) manager 610 monitors the workloadperformance from the exception monitor 615, as compared to the workloadrules 409, and either allows the request to be executed immediately orplaces it in a queue for later execution, as described below, whenpredetermined conditions are met.

If the request is to be executed immediately, the workload query (delay)manager 610 places the requests in buckets 620 _(a) . . . _(s)corresponding to the priority classes to which the requests wereassigned by the administrator 405. A request processor functionperformed under control of a priority scheduler facility (PSF) 625selects queries from the priority class buckets 620 _(a) . . . _(s), inan order determined by the priority associated with each of the buckets620 _(a) . . . _(s), and executes it, as represented by the processingblock 630 on FIG. 6A.

The PSF 625 also monitors the request processing and reports throughputinformation, for example, for each request and for each workgroup, tothe exception monitor 615. Also included is a system condition monitor635, which is provided to detect system conditions, such as nodefailures. The system condition monitor 635 provides the ability todynamically monitor and regulate critical resources in global memory.The exception monitor 615 and system monitor 635 collectively define anexception attribute monitor 640.

The exception monitor 615 compares the throughput with the workloadrules 409 and stores any exceptions (e.g., throughput deviations fromthe workload rules) in the exception log/queue 510. In addition, theexception monitor 615 provides system resource allocation adjustments tothe PSF 625, which adjusts system resource allocation accordingly, e.g.,by adjusting the priority scheduler weights. Further, the exceptionmonitor 615 provides data regarding the workgroup performance againstworkload rules to the workload query (delay) manager 610, which uses thedata to determine whether to delay incoming requests, depending on theworkload group to which the request is assigned.

As can be seen in FIG. 6A, the system provides two feedback loops. Thefirst feedback loop includes the PSF 625 and the exception monitor 615.In this first feedback loop, the system monitors, on a short-term basis,the execution of requests to detect deviations greater than a short-termthreshold from the defined service level for the workload group to whichthe requests were defined. If such deviations are detected, the DBS 100is adjusted, e.g., by adjusting the assignment of system resources toworkload groups.

The second feedback loop includes the workload query (delay) manager610, the PSF 625 and the exception monitor 615. In this second feedbackloop, the system monitors, on a long-term basis, to detect deviationsfrom the expected level of service greater than a long-term threshold.If it does, the system adjusts the execution of requests, e.g., bydelaying, swapping out or aborting requests, to better provide theexpected level of service. Note that swapping out requests is one formof memory control in the sense that before a request is swapped out itconsumes memory and after it is swapped out it does not. While this isthe preferable form of memory control, other forms, in which the amountof memory dedicated to an executing request can be adjusted as part ofthe feedback loop, are also possible.

FIG. 6B illustrates an alternative embodiment and additional detailsrelating to the components and processing performed by a multi-systemvirtual regulator 415 in accordance with one or more embodiments of theinvention. The multi-system workload management process may consist ofthe following architectural components: database system manager 642,system/workload rules 409, system events, system events monitor 635,system state manager 644, system queue table 646, interfaces 648 tocreate/remove dynamic system events, and a multi-system regulator 415.Each of these components is described in further detail below.

Database system manager 642—Each database system 100 contains a databasesystem manager (DBSM) process 642 that regulates the workload of thesystem 100 based on the system rules and system events 409.

System/workload rules 409—Each database system 100 has a set of rulesthat define states based on time periods and system conditions, tasklimits per state, and task priorities per state. Task limits limit thenumber of jobs that can run based on user, account, or some othercriteria. Task priorities define the priority in which each job will runbased on user, account, or some other criteria.

System Events—Each database system 100 has a set of defined events thatdefine a system condition, an event trigger, and an action. Systemconditions include response time goals, CPU usage, nodes down, systemthroughput, and system resource utilization. An action is an action toperform when the event is triggered. (Actions include sending an alert,posting a message to a queue table, changing the system state.)

System Events Monitor 635—Each database system 100 has a System EventsMonitor 635 that is checking system conditions against the system eventsand performing the actions. The Systems Events Monitor 635 posts eventmessages to the System Queue Table 646 to alert the multi-systemsregulator 415 of a system change.

System State Manager 644—Each database system 100 has a System StateManager 644 that adjusts the state of the system 100 (workloadpriorities and limits) based on the system events.

System Queue Table 646—The System Queue Table (SQT) 646 provides theinterface between the System Events Monitor 635 and the Multi-SystemRegulator 415. It is a message queue for sending and receiving messages.

Interfaces 648 to Create/Remove Dynamic System Events—SQL eventinterfaces (SEI) 648 provide the capability to create or remove adynamic system event. A dynamic system event can perform all the actionsof a normal system event include sending an alert, posting a message toa queue table, changing the system state. A dynamic system eventprovides the multi-system regulator 415 the capability to adjust thestate of a single system 100.

Multi-System Regulator 415—As described above, the Multi-SystemRegulator 415 is a process that monitors and adjusts the states of oneor more systems 100 based on the system conditions of each of thesystems 100.

With each of the components described above, embodiments of theinvention can provide a multi-system workload management process. Thefollowing describes the architectural flow (steps) of such a process.

1. The multi-system regulator 415 waits on the system queue table 646 ofeach database system 100 for event messages from the system 100.

2. Each database system 100 has a system event monitor 635 that iscomparing system 100 activity, utilization and resources against definedsystem events 409. When a system event 409 is triggered, the systemevent monitor 635 posts a message on the system queue table 646.

3. The multi-system regulator 415 receives a message from the systemqueue table 646. Based on the message type, the multi-system regulator415 creates a dynamic event on one or more systems 100 using the SQLevent interfaces 648.

4. The creation of the dynamic event causes the system state manager 644to adjust the state of the database system 100 to the desired set ofworkload priorities and task limits.

5. When the system event monitor 635 determines that system conditions409 have returned to a normal condition, the monitor 635 posts an endmessage on the system queue table 646.

6. The multi-system regulator 415 receives the message from the systemqueue table 646. The regulator 415 then uses the SQL event interfaces648 to remove the dynamic event.

7. The removal of the dynamic event causes the system state manager 644to return the database system 100 to the normal state.

The workload query (delay) manager 610, shown in greater detail in FIG.7, receives an assigned request as an input. A comparator 705 determinesif the request should be queued or released for execution. It does thisby determining the workload group assignment for the request andcomparing that workload group's performance against the workload rules,provided by the exception monitor 615. For example, the comparator 705may examine the concurrency level of requests being executed under theworkload group to which the request is assigned. Further, the comparatormay compare the workload group's performance against other workloadrules.

If the comparator 705 determines that the request should not beexecuted, it places the request in a queue 710 along with any otherrequests for which execution has been delayed. The comparator 705continues to monitor the workgroup's performance against the workloadrules and when it reaches an acceptable level, it extracts the requestfrom the queue 710 and releases the request for execution. In somecases, it is not necessary for the request to be stored in the queue towait for workgroup performance to reach a particular level, in whichcase it is released immediately for execution.

Once a request is released for execution it is dispatched (block 715) topriority class buckets 620 _(a) . . . _(s), where it will awaitretrieval and processing 630 by one of a series of AMP Worker Tasks(AWTs) within processing block 630. An AWT is a thread/task that runsinside of each virtual AMP. An AWT is generally utilized to processrequests/queries from users, but may also be triggered or used byinternal database software routines, such as deadlock detection.

The exception monitor 615, receives throughput information from the AWT.A workload performance to workload rules comparator 705 compares thereceived throughput information to the workload rules and logs anydeviations that it finds in the exception log/queue 510. It alsogenerates the workload performance against workload rules informationthat is provided to the workload query (delay) manager 610.

Pre-allocated AWTs are assigned to each AMP and work on a queue system.That is, each AWT waits for work to arrive, performs the work, and thenreturns to the queue and waits for more work. Due to their statelesscondition, AWTs respond quickly to a variety of database executionneeds. At the same time, AWTs serve to limit the number of activeprocesses performing database work within each AMP at any point in time.In other words, AWTs play the role of both expeditor and governor ofrequests/queries.

AMP worker tasks are one of several resources that support the parallelperformance architecture within the Teradata database. AMP worker tasksare of a finite number, with a limited number available to perform newwork on the system. This finite number is an orchestrated part of theinternal work flow management in Teradata. Reserving a special set ofreserve pools for single and few-AMP queries may be beneficial foractive data warehouse applications, but only after establishing a needexists. Understanding and appreciating the role of AMP worker tasks,both in their availability and their scarcity, leads to the need for amore pro-active management of AWTs and their usage.

AMP worker tasks are execution threads that do the work of executing aquery step, once the step is dispatched to the AMP. They also pick upthe work of spawned processes, and of internal tasks such as errorlogging or aborts. Not being tied to a particular session ortransaction, AMP worker tasks are anonymous and immediately reusable andare able to take advantage of any of the CPUs. Both AMPs and AWTs haveequal access to any CPU on the node. A fixed number of AWTs arepre-allocated at startup for each AMP in the configuration, with thedefault number being 80. All of the allocated AWTs can be active at thesame time, sharing the CPUs and memory on the node.

When a query step is sent to an AMP, that step acquires a worker taskfrom the pool of available AWTs. All of the information and contextneeded to perform the database work is contained within the query step.Once the step is complete, the AWT is returned to the pool. If all AMPworker tasks are busy at the time the message containing the new steparrives, then the message will wait in a queue until an AWT is free.Position in the queue is based first on work type, and secondarily onpriority, which is carried within the message header. Priority is basedon the relative weight that is established for the PSF 625 allocationgroup that controls the query step. Too much work can flood the best ofdatabases. Consequently, all database systems have built-in mechanismsto monitor and manage the flow of work in a system. In a paralleldatabase, flow control becomes even more pressing, as balance is onlysustained when all parallel units are getting their fair portion ofresources.

The Teradata database is able to operate near the resource limitswithout exhausting any of them by applying control over the flow of workat the lowest possible level in the system. Each AMP monitors its ownutilization of critical resources, AMP worker tasks being one. If noAWTs are available, it places the incoming messages on a queue. Ifmessages waiting in the queue for an AWT reach a threshold value,further message delivery is throttled for that AMP, allowing workalready underway to complete. Other AMPs continue to work as usual.

One technique that has proven highly effective in helping Teradata toweather extremely heavy workloads is having a reasonable limit on thenumber of active tasks on each AMP. The theory behind setting a limit onAWTs is twofold: 1) that it is better for overall throughput to put thebrakes on before exhaustion of all resources is reached; and 2) keepingall AMPs to a reasonable usage level increases parallel efficiency.However this is not a reasonable approach in a dynamic environment.

Ideally, the minimum number of AWTs that can fully utilize the availableCPU and I/O are employed. After full use of resources has been attained,adding AWTs will only increase the effort of sharing. As standardqueuing theory teaches, when a system has not reached saturation,newly-arriving work can get in, use its portion of the resources, andget out efficiently. However, when resources are saturated, allnewly-arriving work experiences delays equal to the time it takessomeone else to finish their work. In the Teradata database, the impactof any delay due to saturation of resources may be aggravated in caseswhere a query has multiple steps, because there will be multiple placeswhere a delay could be experienced.

In one particular implementation of the Teradata database, 80 (eighty)is selected as the maximum number of AWTs, to provide the best balancebetween AWT overhead and contention and CPU and I/O usage. Historically,80 has worked well as a number that makes available a reasonable numberof AWTs for all the different work types, and yet supports up to 40 or50 new tasks per AMP comfortably. However, managing AWTs is not always asolution to increased demands on the DBS 100. In some cases, anincreased demand on system resources may have an underlying cause, suchthat simply increasing the number of available AWTs may only serve totemporarily mask, or even worsen the demand on resources.

For example, one of the manifestations of resource exhaustion is alengthening queue for processes waiting for AWTs. Therefore, performancemay degrade coincident with a shortage of AWTs. However, this may not bedirectly attributable to the number of AWTs defined. In this case,adding AWTs will tend to aggravate, not reduce, performance issues.

Using all 80 AWTs in an on-going fashion is a symptom that resourceusage is being sustained at a very demanding level. It is one of severalsigns that the platform may be running out of capacity. Adding AWTs maybe treating the effect, but not helping to identify the cause of theperformance problem. On the other hand, many Teradata database systemswill reach 100% CPU utilization with significantly less than 50 activeprocesses of the new work type. Some sites experience their peakthroughput when 40 AWTs are in use servicing new work. By the time manysystems are approaching the limit of 80 AWTs, they are already atmaximum levels of CPU or I/O usage.

In the case where the number of AWTs is reaching their limit, it islikely that a lack of AWTs is merely a symptom of a deeper underlyingproblem or bottleneck. Therefore, it is necessary to carry out a morethorough investigation of all events in the DBS 100, in an attempt tofind the true source of any slowdowns. For example, the underlying or“real” reason for an increase in CPU usage or an increase in the numberof AWTs may be a hardware failure or an arrival rate surge.

Another issue that can impact system-wide performance is a workloadevent, such as the beginning or conclusion of a load or anothermaintenance job that can introduce locks or other delays into the DBS100 or simply trigger the need to change the workload management schemefor the duration of the workload event. The DBS 100 provides a scheduledenvironment that manages priorities and other workload managementcontrols in operating “windows” that trigger at certain times of theday, week, and/or month, or upon receipt of a workload event.

To manage workloads among these dynamic, system-wide situations, it isimportant to firstly classify the types of various system events thatcan occur in a DBS 100, in order to better understand the underlyingcauses of inadequate performance. As shown in FIG. 8, a plurality ofconditions and events are monitored (block 800) and then identified(block 805) so that they can be classified into at least 2 generalcategories:

1. System Conditions (block 810), i.e., system availability orperformance conditions; and

2. Operating Environment Events (block 815).

System Conditions 810 can include a system availability condition, suchas a hardware component failure or recovery, or any other conditionmonitored by a TASM monitored queue. This may include a wide range ofhardware conditions, from the physical degradation of hardware (e.g.,the identification of bad sectors on a hard disk) to the inclusion ofnew hardware (e.g., hot swapping of CPUs, storage media, addition of I/Oor network capabilities, etc). It can also include conditions externalto the DBS 100 as relayed to the DBS 100 from the enterprise, such as anapplication server being down, or a dual/redundant system operating indegraded mode.

System Conditions 810 can also include a system performance condition,such as sustained resource usage, resource depletion, resource skew ormissed Service Level Goals (SLGs).

An example of a system performance condition is the triggering of anaction in response to an ongoing use (or non-use) of a system resource.For example, if there is low sustained CPU and IO for some qualifyingtime, then a schedule background task may be allowed to run. This can beachieved by lifting throttle limits, raising priority weights and/orother means. Correspondingly, if the system returns to a high sustaineduse of the CPU and IO, then the background task is reduced (e.g.,terminated, priority weights lowered, throttle limits lowered, etc).

Another example of a system performance condition is where a conditionis detected due to an increase in the time taken to process a givenindividual request or workload group. For example, if the averageresponse time is greater than the SLG for a given time interval, thenthere may be an underlying system performance condition.

Yet another example may be a sudden increase in the number of AWTsinvoked (as described earlier).

In other words, system performance conditions can include the following:

-   -   1. Any sustained high or low usage of a resource, such as high        CPU usage, high IO usage, a higher than average arrival rate, or        a high concurrency rate;    -   2. Any unusual resource depletion, such as running out of AWTs,        problems with flow control, and unusually high memory usage;    -   3. Any system skew, such as overuse of a particular CPU in a CPU        cluster, or AWT overuse in a AWT cluster; and    -   4. Missed SLGs.

The second type of detection is an Operating Environment Event 815. Suchevents can be predetermined or scheduled, in that a user oradministrator of the system predefines the event at some point duringthe operation of the DBS 100. However, in some instances, OperatingEnvironment Events 815 can occur without any appreciable notice beinggiven to the DBS 100 or to users. The event may be time based, businessevent based or based on any other suitable criteria.

Operating Environment Events 815 can also be defined and associated withthe beginning and completion of a particular application job. Auser-defined event can be sent by the application and received by theDBS 100. This triggers the regulator of the DBS 100 to operate in theruleset's working values associated with this event. For example, theworking values could direct the DBS 100 to give higher priority toworkloads associated with month-end processing, or lower priorityassociated with workloads doing “regular” work, to enable throttles fornon-critical work, and enable filters on workloads that interfere withmonth-end processing reporting consistency such as might happen whendata is being updated while it is being reported on.

In another example, a user may define actions associated with the startof a daily load against a table X. This request triggers a phased set ofactions:

-   -   1. Upon the “Begin Acquisition Phase” of MultiLoad to Table X;        -   Promote the priority of all queries that involve table X;        -   At the same time, restrict the ability for new queries            involving table X from starting until after the data load is            completed. Do this through delay, scheduling or disallowing            the query upon request;    -   2. Upon completion of the acquisition phase and the beginning of        the “Apply Phase”, previously promoted queries that are still        running are aborted (“Times Up!”);    -   3. Upon completion of data load, lift restrictions on queries        involving table X, and allow scheduled and delayed queries to        resume.

Another example is to allow the user to define and automate rulesetworking value changes based on a user-event (rather than resource ortime changes). For example, users may want resource allocation to changebased on a business calendar that treats weekends and holidaysdifferently from weekdays, and normal processing differently fromquarterly or month-end processing.

As these events are generally driven by business or user considerations,and not necessarily by hardware or software considerations, they aredifficult to predict in advance.

Thus, upon detection of any of System Conditions 810 or OperatingEnvironments Events 815, one or more actions can be triggered. In thisregard, Block 820 determines whether the detected System Conditions 810or Operating Environments Events 815 are resolvable.

The action taken in response to the detection of a particular conditionor event will vary depending on the type of condition or event detected.The automated action will fall into one of four broad categories (asshown in FIG. 8):

1. Notify (block 825);

2. Change the Workload Management Ruleset's Working Values (block 830);

3. Initiate an automated response (block 835); and

4. Log the event or condition, if the condition or event is notrecognized (block 840).

Turning to the first possible automated action, the system may notifyeither a person or another software application/component including,users, the DBA, or a reporting application. Notification can be throughone or more notification approaches:

Notification through a TASM event queue monitored by some otherapplication (for example, “tell users to expect slow response times”);

Notification through sending an Alert; and/or

Notification (including diagnostic drill-down) through automationexecution of a program or a stored procedure.

Notification may be preferable where the system has no immediate way inwhich to ameliorate or rectify the condition, or where a user'sexpectation needs to be managed.

A second automated action type is to change the Workload ManagementRuleset's working values.

FIG. 9 illustrates how conditions and events may be comprised ofindividual conditions or events and condition or event combinations,which in turn cause the resulting actions.

The following is a table that represents kinds of conditions and eventsthat can be detected.

Class Type Description Operating (Time) Period These are the currentPeriods representing intervals of Environment time during the day, week,or month. The system Event monitors the system time, automaticallycausing an event when the period starts, and it will last until theperiod ends. User Defined These are used to report anything that couldconceivably (External)* change an operating environment, such asapplication events. They last until rescinded or optionally time out.System Performance DBS 100 components degrade or fail, or resources goCondition and below some threshold for some period of time. TheAvailability system will do the monitoring of these events. Oncedetected, the system will keep the event in effect until the componentis back up or the resource goes back above the threshold value for someminimal amount of time. User Defined These are used to report anythingthat could conceivably (External)* change a system condition, such asdual system failures. They last until rescinded or optionally time out.

Operating Environment Events and System Condition combinations arelogical expressions of states. The simplest combinations are comprisedof just one state. More complex combinations can be defined that combinemultiple states with two or more levels of logical operators, forexample, given four individual states, e1 through e4:

Operator Levels Logical Expression 0 e1 1 e1 OR e2 1 e1 AND e2 2 (e1 ORe2) AND (e3 OR e4) 2 (e1 AND e2 AND (e3 OR e4))

Combinations cause one more actions when the logical expressions areevaluated to be “true.” The following table outlines the kinds ofactions that are supported.

Type Description Alert Use the alert capability to generate an alert.Program Execute a program to be named. Queue Table Write to a (wellknown) queue table. SysCon Change the System Condition. OpEnv Change theOperating Environment.

As shown in FIG. 10, the DBS 100 has a number of rules (in aggregationtermed a ruleset) which define the way in which the DBS 100 operates.The rules include a name (block 1000), attributes (block 1005), whichdescribes what the rules do (e.g., session limit on user Jane) andworking values (WVs) (block 1010), which are flags or values thatindicate whether the rule is active or not and the particular setting ofthe value. A set of all WVs for all the rules contained in a Ruleset iscalled a “Working Value Set (WVS).”

A number of “states” can be defined, each state being associated with aparticular WVS (i.e., a particular instance of a rule set). By swappingstates, the working values of the workload management ruleset arechanged.

This process is best illustrated by a simple example. At FIG. 10, thereis shown a particular WVS which, in the example, is associated with theState “X.” State X, in the example, is a state that is invoked when thedatabase is at almost peak capacity, Peak capacity, in the presentexample, is determined by detecting one of two events, namely that thearrival rate of jobs is greater than 50 per minute, or alternatively,that there is a sustained CPU usage of over 95% for 600 seconds. State Xis designed to prevent resources being channeled to less urgent work. InState X, Filter A (block 1015), which denies access to table “Zoo”(which contains cold data and is therefore not required for urgentwork), is enabled. Furthermore, Throttle M (block 1020), which limitsthe number of sessions to user “Jane” (a user who works in the marketingdepartment, and therefore does not normally have urgent requests), isalso enabled. State “X” is therefore skewed towards limiting theinteraction that user Jane has with the DBS 100, and is also skewedtowards limiting access to table Zoo, so that the DBS 100 can allocateresources to urgent tasks in preference to non-urgent tasks.

A second State “Y” (not shown) may also be created. In State “Y”, thecorresponding rule set disables filter “A”, and increases Jane's sessionlimit to 6 concurrent sessions. Therefore, State “Y” may only be invokedwhen resource usage falls below a predetermined level. Each state ispredetermined (i.e., defined) beforehand by a DBA. Therefore, eachruleset, working value set and state requires some input from a user oradministrator that has some knowledge of the usage patterns of the DBS100, knowledge of the data contained in the database, and perhaps evenknowledge of the users. Knowledge of workloads, their importance, theircharacteristic is most likely required more so than the sameunderstanding of individual rules. Of course, as a user definesworkloads, most of that has already come to light, i.e., what users andrequests are in a workload, how important or critical is the workload,etc. A third action type is to resolve the issue internally. Resolutionby the DBS 100 is in some cases a better approach to resolving issues,as it does not require any input from a DBA or a user to definerules-based actions.

Resolution is achieved by implementing a set of internal rules which areactivated on the basis of the event detected and the enforcementpriority of the request along with other information gathered throughthe exception monitoring process.

Some examples of automated action which result in the automaticresolution of issues are given below. This list is not exhaustive and ismerely illustrative of some types of resolution.

For the purposes of this example, it is assumed that the event that isdetected is a longer than average response time (i.e., an exceptionmonitor 615 detects that the response time SLG is continually exceed fora given time and percentage). The first step in launching an automatedaction is to determine whether an underlying cause can be identified.

For example, is the AWT pool the cause of the longer than averageresponse time? This is determined by seeing how many AWTs are beingused. If the number of idle or inactive AWTs is very low, the AWT poolis automatically increased to the maximum allowed (normally 80 in atypical Teradata system).

The SLG is then monitored to determine whether the issue has beenameliorated. When the SLG is satisfactory for a qualifying time, the AWTpoolsize is progressively decreased until a suitable workable value isfound.

However, the AWT pool may not be the cause of the event. Through themeasuring of various system performance indicators, it may be found thatthe Arrival Rate is the cause of decreased performance. Therefore,rather than limiting on concurrency, the DBS 100 can use thisinformation to take the action of limiting the arrival rate (i.e.,throttle back the arrival rate to a defined level, rather than allowingqueries to arrive at unlimited rates). This provides an added ability tocontrol the volume of work accepted per WD.

Alternatively, there may be some WDs at same or lower enforcementexceeding their anticipated arrival rates by some qualifying time andamount. This is determined by reviewing the anticipated arrival rate asdefined by the SLG.

If there are WDs at the same or lower enforcement exceeding theiranticipated arrival rates, the WD's concurrency level is decreased to aminimum lower limit.

The SLG is then monitored, and when the SLG returns to a satisfactorylevel for a qualifying time, the concurrency level is increased to adefined normal level (or eliminated if no concurrency level was definedoriginally).

If the event cannot be easily identified or categorized by the DBS 100,then the event is simply logged as a “un-resolvable” problem. Thisprovides information which can be studied at a later date by a userand/or DBA, with a view to identifying new and systemic problemspreviously unknown.

The embodiment described herein, through a mixture of detection andmanagement techniques, seeks to correctly manage users' expectations andconcurrently smooth the peaks and valleys of usage. Simply being awareof the current or projected usage of the DBS 100 may be a viablesolution to smoothing peaks and valleys of usage. For example, if a userknows that he needs to run a particular report “sometime today,” he mayavoid a high usage (and slow response) time in the morning in favor of alower usage time in the afternoon. Moreover, if the work cannot bedelayed, insight into DBS 100 usage can, at the very least, help setreasonable expectations.

Moreover, the predetermined response to events, through the invocationof different “states” (i.e., changes in the ruleset's working values)can also assist in smoothing peaks and valleys of usage. The embodimentdescribed herein additionally seeks to manage automatically to bettermeet SLGs, in light of extenuating circumstances such as hardwarefailures, enterprise issues and business conditions.

However, automated workload management needs to act differentlydepending on what states are active on the system at any given time.Each unique combination of conditions and events could constitute aunique state with unique automated actions. Given a myriad of possiblecondition and event types and associated values, a combinatorialexplosion of possible states can exist, making rule-based automatedworkload management a very daunting and error-prone task. For example,given just 15 different condition and event types that get monitored,each with a simple on or off value, there can be as many as 2¹⁵=32,768possible combinations of states. This number only increases as thenumber of unique condition and event types or the possible values ofeach monitored condition or event type increases.

A DBA managing the rules-based management system, after identifying eachof these many states must also to designate a unique action for eachstate. The DBA would further need to associate priority to each statesuch that if more than one state were active at a given time, theautomated workload management scheme would know which action takesprecedence if the actions conflict. In general, the DBA would find thesetasks overwhelming or even impossible, as it is extremely difficult tomanage such an environment.

To solve this problem associated with automated workload management, orany rule-driven system in general, the present invention introduces ann-dimensional matrix to tame the combinatorial explosion of states andto provide a simpler perspective to the rules-based environment.Choosing two or more well-known key dimensions provides a perspectivethat guides the DBA to know whether or not he has identified all theimportant combinations, and minimizes the number of unique actionsrequired when various combinations occur. Given that n<total possibleevent types that can be active, each unique event or event combinationis collapsed into a finite number of one of the n-dimension elements.

In one embodiment, for example, as shown in FIG. 11, a two-dimensionalstate matrix 1100 may be used, wherein the first dimension 1105represents the System Condition (SysCon) and the second dimension 1110represents the Operating Environment Events (OpEnv). As noted above,System Conditions 1105 represent the “condition” or “health” of thesystem, e.g., degraded to the “red” system condition because a node isdown, while Operating Environment Events 1110 represent the “kind ofwork” that the system is being expected to perform, e.g., within anInteractive or Batch operational environment, wherein Interactive takesprecedence over Batch.

Each element 1115 of the state matrix 1100 is a <SysCon, OpEnv> pairthat references a workload management state, which in turn invokes asingle WVS instance of the workload management ruleset. Multiple matrix1100 elements may reference a common state and thus invoke the same WVSinstance of the workload management ruleset. However, only one state isin effect at any given time, based on the matrix 1100 element 1115referenced by the highest SysCon severity and the highest OpEnvprecedence in effect. On the other hand, a System Condition, OperatingEnvironment Event, or state can change as specified by directivesdefined by the DBA. One of the main benefits of the state matrix 1100 isthat the DBA does not specify a state change directly, but must do soindirectly through directives that change the SysCon or OpEnv.

When a particular condition or event combination is evaluated to betrue, it is mapped to one of the elements 1115 of one of the dimensionsof the matrix 1100. For example, given the condition “if AMP WorkerTasks available is less than 3 and Workload X's Concurrency is greaterthan 100” is “true,” it may map to the System Condition of RED. Inanother example, an event of “Monday through Friday between 7 AM and 6PM” when “true” would map to the Operating Environment Event ofOPERATIONAL_QUERIES.

The combination of <RED, OPERATIONAL_QUERIES>, per the correspondingmatrix 1100 element 1115, maps to a specific workload management state,which in turn invokes the WVS instance of the workload managementruleset named WVS #21. Unspecified combinations would map to a defaultSystem Condition and a default Operating Environment.

Further, a state identified in one element 1115 of the matrix 1100 canbe repeated in another element 1115 of the matrix 1100. For example, inFIG. 11, WVS #33 is the chosen workload management rule when the<SysCon, OpEnv> pair is any of: <RED, QUARTERLY_PROCESSING>, <YELLOW,QUARTERLY_PROCESSING> or <RED, END_OF_WEEK_PROCESSING>.

The effect of all this is that the matrix 1100 manages all possiblestates. In the example of FIG. 11, 12 event combinations comprise2¹²=4096 possible states. However, the 2-dimensional matrix 1100 of FIG.11, with 3 System Conditions and 4 Operating Environment Events, yieldsat the most 4×3=12 states, although less than 12 states may be usedbecause of the ability to share states among different <SysCon, OpEnv>pairs in the matrix 1100.

In addition to managing the number of states, the matrix 1100facilitates conflict resolution through prioritization of itsdimensions, such that the system conditions' positions and operatingenvironment events' positions within the matrix 1100 indicate theirprecedence.

Suppose that more than one condition or event combination were true atany given time. Without the state matrix 1100, a list of 4096 possiblestates would need to be prioritized by the DBA to determine whichworkload management rules should be implemented, which would be adaunting task. The matrix 1100 greatly diminishes this challenge throughthe prioritization of each dimension.

For example, the values of the System Condition dimension are Green,Yellow and Red, wherein Yellow is more severe, or has higher precedenceover Green, and Red is more severe or has higher precedence over Yellowas well as Green. If two condition and event combinations were toevaluate as “true” at the same time, one thereby mapping to Yellow andthe other mapping to Red, the condition and event combination associatedwith Red would have precedence over the condition and event combinationassociated with Yellow.

Consider the following examples. In a first example, there may be aconflict resolution in the System Condition dimension between “Red,”which has precedence (e.g., is more “severe”) over “Yellow.” If a nodeis down/migrated, then a “Red” System Condition exists. If a dual systemis down, then a “Yellow” System Condition exists. If a node isdown/migrated and a dual system is down, then the “Red” System Conditionhas precedence.

In a second example, there may be a conflict resolution in the OperatingEnvironment Event dimension between a “Daily Loads” event, which hasprecedence over “Operational Queries” events. At 8 AM, the OperatingEnvironment Event may trigger the “Operational Queries” event. However,if loads are running, then the Operating Environment Event may alsotrigger the “Daily Loads” event. If it is 8 AM and the loads are stillrunning, then the “Daily Loads” Operating Environment Event takesprecedence.

Once detected, it is the general case that a condition or event statusis remembered (persists) until the status is changed or reset. However,conditions or events may have expiration times, such as for user-definedconditions and events, for situations where the status fails to resetonce the condition or event changes. Moreover, conditions or events mayhave qualification times that require the state be sustained for someperiod of time, to avoid thrashing situations. Finally, conditions orevents may have minimum and maximum duration times to avoid frequent orinfrequent state changes.

In summary, the state matrix 1100 of the present invention has a numberof advantages. The state matrix 1100 introduces simplicity for the vastmajority of user scenarios by preventing an explosion in state handingthrough a simple, understandable n-dimensional matrix. To maintain thissimplicity, best practices will guide the system operator to fewerrather than many SysCon and OpEnv values. It also maintains mastercontrol of WVS on the system, but can also support very complexscenarios. In addition, the state matrix can alternatively support anexternal “enterprise” master through user-defined functions andnotifications. Finally, the state matrix 1100 is intended to provideextra dimensions of system management using WD-level rules with adynamic regulator.

A key point of the matrix 1100 is that by limiting actions to onlychange SysCon or OpEnv (and not states, or individual rules, or rules'WVs), master control is contained in a single place, and avoids havingtoo many entities asserting control. For example, without this, a usermight change the individual weight of one workload to give it highestpriority, without understanding the impact this has on other workloads.Another user might change the priority of another workload to be evenhigher, such that they overwrite the intentions of the first user. Then,the DBS 100 internally might have done yet different things. Byfunneling all actions to be associated with a SysCon or OpEnv instead ofdirected to individual rules in the ruleset, or directly to a state as awhole, the present invention avoids what could be chaos in the variousevents. Consequently, in the present invention, the WVS's are changed asa whole (since some settings must really be made in light of allworkloads, not a single workload or other rule), and by changing justSysCon or OpEnv, in combination with precedence, conflict resolution ismaintained at the matrix 1100.

The state matrix 1100 may be used by a single regulator 415 controllinga single DBS 100, or a plurality of state matrices 1100 may be used by aplurality of regulators 415 controlling a plurality of DBS 100.Moreover, a single state matrix 1100 may be used with a plurality ofregulators 415 controlling a plurality of DBS 100, wherein the singlestate matrix 1100 is a domain-level state matrix 1100 used by adomain-level “virtual” regulator.

FIG. 12 illustrates an embodiment where a plurality of regulators 415exist in a domain 1200 comprised of a plurality of dual-active DBS 100,wherein each of the dual-active DBS 100 is managed by one or moreregulators 415 and the domain 1200 is managed by one or moremulti-system “virtual” regulators 415.

Managing system resources on the basis of individual systems andrequests does not, in general, satisfactorily manage complex workloadsand SLGs across a domain 1200 in a multi-system environment. Toautomatically achieve workload goals in a multi-system environment,performance goals must first be defined (administered), then managed(regulated), and finally monitored across the entire domain 1200 (set ofsystems participating in an n-system environment).

Regulators 415 are used to manage workloads on an individual DBS 100basis. A virtual regulator 415 comprises a modified regulator 415implemented to enhance the closed-loop system management (CLSM)architecture in a domain 1200. That is, by extending the functionalityof the regulator 415 components, complex workloads are manageable acrossa domain 1200.

The function of the virtual regulator 415 is to control and manageworkloads across all DBS 100 in a domain 1200. The functionality of thevirtual regulator 415 extends the existing goal-oriented workloadmanagement infrastructure, which is capable of managing various types ofworkloads encountered during processing.

In one embodiment, the virtual regulator 415 includes a “thin” versionof a DBS 100, where the “thin” DBS 100 is a DBS 100 executing in anemulation mode, such as described in U.S. Pat. Nos. 6,738,756,7,155,428, 6,801,903 and 7,089,258, all of which are incorporated byreference herein. A query optimizer function 320 of the “thin” DBS 100allows the virtual regulator 415 to classify received queries into “who,what, where” classification criteria, and allows a workload querymanager 610 of the “thin” DBS 100 to perform the actual routing of thequeries among multiple DBS 100 in the domain 1200. In addition, the useof the “thin” DBS 100 in the virtual regulator 415 provides a scalablearchitecture, open application programming interfaces (APIs), externalstored procedures (XSPs), user defined functions (UDFs), messagequeuing, logging capabilities, rules engines, etc.

The virtual regulator 415 also includes a set of open APIs, known as“Traffic Cop” APIs, that provide the virtual regulator 415 with theability to monitor DBS 100 states, to obtain DBS 100 status andconditions, to activate inactive DBS 100, to deactivate active DBS 100,to set workload groups, to delay queries (i.e., to control or throttlethroughput), to reject queries (i.e., to filter queries), to summarizedata and statistics, to create DBQL log entries, run a program (storedprocedures, external stored procedures, UDFs, etc.), to send messages toqueue tables (Push, Pop Queues), and to create dynamic operating rules.The Traffic Cop APIs are also made available to all of the regulators415 for each DBS 100, thereby allowing the regulators 415 for each DBS100 and the virtual regulator 415 for the domain 1200 to communicatethis information between themselves.

Specifically, the virtual regulator 415 performs the followingfunctions: (a) Regulate (adjust) system conditions (resources, settings,PSF weights, etc.) against workload expectations (SLGs) across thedomain 1200, and to direct query traffic to any of the DBS 100 via a setof predefined rules. (b) Monitor and manage system conditions across thedomain 1200, including adjusting or regulating response timerequirements by DBS 100, as well as using the Traffic Cop APIs to handlefilter, throttle and/or dynamic allocation of resource weights withinDBS 100 and partitions so as to meet SLGs across the domain 1200. (c)Raise an alert to a DBA for manual handling (e.g., defer or executequery, recommendation, etc.) (d) Cross-compare workload response timehistories (via a query log) with workload SLGs across the domain 1200 todetermine if query gating (i.e., flow control) through altered TrafficCop API settings presents feasible opportunities for the workload. (e)Manage and monitor the regulators 415 across the domain 1200 using theTraffic Cop APIs, so as to avoid missing SLGs on currently executingworkloads, or to allow workloads to execute the queries while missingSLGs by some predefined or proportional percentage based on shortage ofresources (i.e., based on predefined rules). (f) Route queries (traffic)to one or more available DBS 100.

Although FIG. 12 depicts an implementation using a single virtualregulator 415 for the entire domain 1200, in some exemplaryenvironments, one or more backup virtual regulators 415 are alsoprovided for circumstances where the primary virtual regulator 415malfunctions or is otherwise unavailable. Such backup virtual regulators415 may be active at all times or may remain dormant until needed.

In some embodiments, each regulator 415 communicates its systemconditions and operating environment events directly to the virtualregulator 415. The virtual regulator 415 compiles the information, addsdomain 1200 or additional system level information, to the extent thereis any, and makes its adjustments based on the resulting set ofinformation.

In other embodiments, each regulator 415 may have superordinate and/orsubordinate regulators 415. In such embodiments, each regulator 415gathers information related to its own system conditions and operatingenvironment events, as well as that of its children regulators 415, andreports the aggregated information to its parent regulator 415 or thevirtual regulator 415 at the highest level of the domain 1200.

When the virtual regulator 415 compiles its information with that whichis reported by all of the regulators 415, it will have completeinformation for domain 1200. The virtual regulator 415 analyzes theaggregated information to apply rules and make adjustments.

The virtual regulator 415 receives information concerning the states,events and conditions from the regulators 415, and compares thesestates, events and conditions to the SLGs. In response, the virtualregulator 415 adjusts the operational characteristics of the various DBS100 through the set of “Traffic Cop” Open APIs to better address thestates, events and conditions of the DBS 100 throughout the domain 1200.

Generally speaking, regulators 415 provide real-time closed-loop systemmanagement over resources within the DBS 100, with the loop having afairly narrow bandwidth, typically on the order of milliseconds,seconds, or minutes. The virtual regulator 415, on the other hand,provides real-time closed-loop system management over resources withinthe domain 1200, with the loop having a much larger bandwidth, typicallyon the order of minutes, hours, or days.

Further, while the regulators 415 control resources within the DBS's100, and the virtual regulator 415 controls resources across the domain1200, in many cases, DBS 100 resources and domain 1200 resources are thesame. The virtual regulator 415 has a higher level view of resourceswithin the domain 1200, because it is aware of the state of resources ofall DBS 100, while each regulator 415 is generally only aware of thestate of resources within its own DBS 100.

There are a number of techniques by which virtual regulator 415implements its adjustments to the allocation of system resources. Forexample, and as illustrated in FIG. 12, the virtual regulator 415communicates adjustments directly to the regulators 415 for each DBS100, and the regulators 415 for each DBS 100 then apply the relevantrule adjustments. Alternatively, the virtual regulator 415 communicatesadjustments to the regulators 415 for each DBS 100, which then passesthem on to other, e.g., subordinate, regulators 415 in other DBS 100. Ineither case, the regulators 415 in each DBS 100 incorporate adjustmentscommunicated by the virtual regulator 415.

Given that the virtual regulator 415 has access to the state, event andcondition information from all DBS 100, it can make adjustments that aremindful of meeting SLGs for various workload groups. It is capable of,for example, adjusting the resources allocated to a particular workloadgroup on a domain 1200 basis, to make sure that the SLGs for thatworkload group are met. It is further able to identify bottlenecks inperformance and allocate resources to alleviate the bottlenecks. Also,it selectively deprives resources from a workload group that is idlingresources. In general, the virtual regulator 415 provides a domain 415view of workload administration, while the regulators 415 in each DBS100 provide a system view of workload administration.

The present invention also provides for dynamic query optimizationbetween DBS 100 in the domain 1200 based on system conditions andoperating environment events. In the domain 1200, the DBS 100 to which aquery will be routed can be chosen by the virtual regulator 415; in asingle DBS 100, there is no choice and the associated regulator 415 forthat DBS 100 routes only within that DBS 100.

This element of choice can be leveraged to make intelligent decisionsregarding query routing that are based on the dynamic state of theconstituent DBS 100 within the domain 1200. Routing can be based anysystem conditions or operating environment events that are viewed aspertinent to workload management and query routing. This solution thusleverages and provides a runtime resource sensitive and data drivenoptimization of query execution.

In one embodiment, the system conditions or operating environment eventsmay comprise:

-   -   Performance conditions, such as:        -   Flow control,        -   AWT exhaustion, or        -   Low memory.    -   Availability indicators, such as:        -   Performance continuity situation,        -   System health indicator,        -   Degraded disk devices,        -   Degraded controllers,        -   Node, parsing engine (PE), access module processor (AMP),            gateway (GTW) or interconnect (BYNET) down,        -   Running in fallback.    -   Resource utilization (the optimizer 320 bias can be set to favor        plans that will use less of the busy resources), such as:        -   Balanced,        -   CPU intensive or under-utilized,        -   Disk I/O intensive or under-utilized, or        -   File system intensive or under-utilized.    -   User or DBA defined conditions or events, or user-defined        events.    -   Time periods (calendar).

Routing can be based on combinations of the system conditions andoperating environment events described above. As noted in the statematrix 1100, associated with each condition, event or combination ofconditions and events can be a WVS instance of a workload managementruleset. Some of the possible rules are:

-   -   Do not route to system X under this condition, event or        combination of conditions and events.    -   Increase optimizer 320 run time estimate for system X by Y % of        prior to routing decision.    -   Use optimizer 320 bias factors to determine optimizer 320        estimates prior to routing decision.    -   Decrease load on system X by routing only Y % of queries that        would normally be routed to system X.

Thus, the present invention adds to the value proposition of amulti-system environment by leveraging query routing choices and makingintelligent choices of query routing based on system conditions andoperating environment events.

The present invention also provides for dynamic query and step routingbetween systems 100 tuned for different objectives. Consider that a datawarehouse system 100 may be tuned to perform well on a particularworkload, but that same tuning may not be optimal for another workload.In a single system 100, tuning choices must be made that trade-off theperformance of multiple workloads. Example workloads would include batchloading, high volume SQL oriented inserts/updates, decision support andtactical queries.

The present invention also provides a solution that allows a domain 1200to be tuned for multiple objectives with few or lesser trade-offs.Specifically, the present invention enables tuning of each constituentsystem/DBS 100 (within a domain 1200) differently and routes queries orsteps of queries to systems 100 based on cost estimates of the moreefficient system/DBS 100. In the case of per step routing, stepcross-overs between systems 100 (the cost of a first step performed on afirst system 100 and a second step performed on a second system 100) arealso costed, in order determine a low cost plan.

The following table provides an example of possible tuning parameters:

Optimized for Optimized for Parameter Decision Support Systems (DSS)tactical queries in an ADW Data Block Size Large - Fetch more rowsSmall - Avoid fetching/or per access caching unneeded rows Cylinder ReadBuffers Many - effectively makes a Few - Disruptive to tactical randomworkload pseudo- query SLGs - Memory use sequential Memory for CacheSmall to Medium - Cost Large - increase hit rate reduction - Scanneddata cache unfriendly Disk Size Larger - Pseudo-sequential Smaller - Toachieve good scan rate allows good performance for performance for DSS -ADW/Capacity Capacity and cost reduction Read-ahead Read ahead many toNo read-ahead or read- generate as much workload ahead few to avoidhaving as possible for the IO uncontrolled DSS workload subsystem, sinceit can over consume IOs and optimize performance when impact tacticalquery there are more IOs queued performance for service

The present invention uses cost functions of each system 100 todetermine routing. Moreover, cost coefficients for each system 100 are afunction of the tuning (for example, block size). Finally, the decisionto route can be based on which system 100 can meet the SLG.

As used herein, a cost or cost function provides an estimate of how manytimes rows must be moved in or out of AMPs (within a system 100). Suchmovement includes row read, row writes, or AMP-AMP row transfers. Thepurpose of a cost analysis is not to unerringly choose the “best”strategy. Rather, if there is a clearly best strategy, it is desirableto find and route a function to a system 100 tuned for such a strategy.Similarly, if there is clearly a worst strategy, it is desirable toavoid a system 100 tuned for such a strategy. Since each system 100 maybe tuned differently, the cost function may be utilized to determinewhich system 100 should be used for a particular query or query step.

Cost-based query optimizers typically consider a large number ofcandidate plans, and select one for execution. The choice of anexecution plan is the result of various interacting factors, such as adatabase and system state, current table statistics, calibration ofcosting formulas, algorithms to generate alternatives of interest,heuristics (e.g., greedy algorithms) to cope with combinatorialexplosion of the search space, cardinalities, etc. In addition, a costmodel can be used to estimate the response time of a given query planand search the space (e.g., state space or the differently tuned systems100) of query plans in some fashion to return a plan (that utilizes oneor more systems 100) with a low (minimum), value of cost. For example, acost function may compute a cost value for a query plan that is forinstance the time needed to execute the plan, the goal of optimizationis to generate the query plan with the lowest cost value. To achieveefficiency, cost models estimate the response time for the variouslytuned systems using approximation functions. Thus, the differentmethod/systems 100 response times for performing a unit of work arecompared and the most efficient (system or systems) is selected.

As a result, the present invention enables new efficiencies in a domain1200 comprising a multi-system environment that are not possible withina single DBS 100. In a single DBS 100, a tuning parameter must be in asingle state, and cannot be in multiple states, which often causes atrade-off between conflicting objectives. However, with a multi-systemenvironment, the trade-offs inherent in tuning a single system 100 arenot present. Major differences in tuning and configuration are possiblebetween the constituent systems 100. When combined with the ability tointelligently route a workload between the systems 100, this inventionopens up new solutions to data warehouse problems.

In addition to routing a workload to particularly tuned systems, someworkloads may still not perform optimally due to the limitedavailability of resources. As described above, data maintenance tasks ina DBS 100 are typically competitors for limited resources and often areoverlooked. Typically, these tasks run in the background or arerelegated to a low system priority as indicated in the states matrix1100. However, in some cases, performing these data maintenance tasksmay actually free up more resources than they consume. For example, thisis true in the case of gathering up-to-date statistics, which may leadto more efficient optimizer plans. In other examples, data maintenancetasks may provide other rewards in terms of space savings, compaction,data integrity, etc. In a multi-system environment of the invention(e.g., a domain 1200), some data maintenance tasks may be performed onone system 100 and the results applied to some or all of the otherssystems 100. Such data maintenance provides the ability to conductcapacity planning of data warehouse systems 100.

One or more embodiments of the invention provide a mechanism for virtualdata maintenance in the DBS 100. In this regard, some types of datamaintenance tasks may be performed virtually on a replicated copy of thedata. Only certain tasks may be suitable for this remote virtualization,including the following:

Collecting statistics,

Collecting demographics,

Compression analysis,

Checking referential integrity,

Space accounting,

Index analysis/wizard, and

Statistics analysis/wizard.

However, other tasks, must be run in-situ on the system to which theyapply (for example, checking the integrity of the file system), and thusmay not be applicable to the present invention.

The present invention includes the ability to route certain datamaintenance tasks for service on one or more designated DBS 100 within adomain 1200 (i.e., multi-system environment). In other words, if thatdata maintenance task is invoked on one DBS 100, the request can bedetected and sent to another DBS 100 for execution. Each of the aboveidentified tasks are described in more detail below.

Collecting statistics or demographics can be an I/O and CPU intensiveactivity that can yield more efficient plans, but which is oftenneglected because of the impact it might cause on a production DBS 100in terms thoughput due to resource use and response time. Somestatistics are global and are always eligible for virtualization to adifferent DBS 100 in a domain 1200. Other statistics are kept on per AMPbasis and are eligible for virtualization on a different DBS 100 as longas the number of AMPs is identical.

Furthermore, through export of a hash map from one DBS 100 to anotherDBS 100, the present invention includes the ability to calculate per AMPstatistics virtually from a DBS 100 with a different number of AMPs thanthe target DBS 100. In this regard, a hash map (and hash function) isused in a partitioning scheme to assign records to AMPs (wherein thehashing function generates a hash “bucket” number and the hash bucketnumbers are mapped to AMPs). Such partitioning is used to enhanceparallel processing across multiple AMPs (by dividing a query or otherunit of work into smaller sub-units, each of which can be assigned to anAMP). Thus, the hash map that is used to assign records to a particularAMP provides the ability to gather statistics regarding how often aparticular AMP will be assigned a workload. Such knowledge is useful indetermining an optimal and efficient plan.

Space accounting data maintenance can be performed virtually when thenumber of parallel units on two DBS's 100 is identical and all objectsare replicated. In this case, the normal background tasks ofrecalculating space usage may be virtualized and remoted from one DBS100 to another DBS 100.

Compression analysis involves the examination of data for frequency ofuse of each value in each column domain. This analysis is independent ofthe number of parallel units in a data warehouse system 100 andtherefore may be virtualized and exported to any DBS 100 in a domain1200 containing a replicated copy of a relational table. The results ofthe compression analysis are applicable to all replicated copies of thedata and may be used to instantiate value compression on each DBS 100 inthe domain 1200.

Integrity checking is often performed at multiple levels, some of whichare suitable for virtualization and execution on a different DBS 100 ina domain 1200. In the Teradata RDBMS product (e.g., an ADW), one suchintegrity checking utility is “Check Table.” “Check Table” is a utilitythat compares primary and fallback copies of redundant data and reportsany discrepancies. Virtualized remote execution of a replica of some orall of a DBS 100 can accomplish some of the data integrity objectives.One may also use a data integrity checking utility to support optionsfor virtualized checking versus in situ testing such that overlap isminimized. In this way, the maximum value can be gained form virtualremote execution of the integrity check.

Index analysis is often recommended to identify opportunities forimproved indexes for a workload. A number of techniques may be employedfor index analysis. One example is the Teradata Index Wizard™ thatautomates the selection of secondary indices for a given workload.Through the use of a Teradata System Emulation Tool (TSET) of theinvention, the DBS 100 cost parameters may be emulated on another system100 in a domain 1200 and the index analysis can be virtualized forremote execution.

Statistics analysis is often recommended to identify opportunities forimproved statistics collection. A number of new techniques may beemployed for statistics analysis—one example is through the use of TSETin accordance with embodiments of the invention. For example, tablelevel statistics can easily be shared from one DBS 100 to another DBS100 in a domain 1200. The statistics analysis can easily be virtualizedfor remote execution.

The present invention thus enables the use of another DBS 100 to executevarious data maintenance tasks virtually on behalf of DBS's 100 where itis undesirable to execute those same data maintenance tasks. Byvirtualizing these data maintenance tasks, they may execute on DBS's 100more desirable for those tasks. The present invention enables new formsof coexistence and new options for capacity planning, whereby dedicationof specific resources for data maintenance tasks may be a more costeffective or performance solution than running those same tasks on theDBS's 100 servicing the bulk of the workload.

FIG. 13 is a flow chart illustrating a method for performing datamaintenance tasks in a domain 1200 in accordance with one or moreembodiments of the invention. As illustrated, a data maintenance requestfor service on one or more designated DBS's 100 in the domain 1200 isinvoked. The virtual regulator 415 detects the request at step 1300. Theregulator 415 routes the request to a different DBS 100 at step 1305. Atstep 1310, the results of the data maintenance operation is received inthe virtual regulator 415 (i.e., from the different DBS 100). At step1315, the results of the operation are applied to the originallydesignated DBS's 100.

In view of the above, it can be seen that the multi-system virtualregulator 415 manages complex workloads in a multi-systemenvironment/domain 1200. Such management cannot be satisfied by simplymanaging system resources on an individual system basis. Toautomatically achieve workload goals across multi-systems, performanceobjectives must first be defined (administered) and then managed(regulated) and monitored across the entire domain 1200 (set of systems100 participating in a multi-system environment).

Thus, as illustrated in FIG. 13, the data maintenance task is invoked onone DBS 100, detected by the virtual regulator 415, and the task is sentto another DBS 100 in the domain 1200 for execution. The routing of suchdata maintenance tasks to different DBS 100 in the domain 1200 allowsmore desirable DBS's 100 to execute data maintenance tasks therebyfreeing up resources, allowing more efficient optimizer plans, savingspace, and/or improving data integrity.

In addition to providing the ability to perform data maintenance tasks,it is desirable for an application or user to dynamically manage andaccess critical database resources. One or more embodiments of theinvention enable such capabilities through a virtual memory tableinterface. The user, via a new logon partition (called a Virtual MemoryPartition), is able to select rows from a virtual processor's (VProc)global memory segment. A new set of TASM open APIs (applicationprogramming interfaces) provides interfaces to the virtual regulator 415and/or administrator 405. These new set of open APIs allow the user toselect critical system information such as: delayed query lists, summarydata and statistics for AWTs, Locks, TIP (Transactions In Progress),Throttles, Filters, etc.

To provide such a capability, embodiments of the invention leverageseveral key components of the above described architecture and combinethe key components with User Defined Functions (UDFs), External StoredProcedures (XSPs), and XML (extensible markup language) functionality.This combined technology enables a regulator 415 to dynamically manageand monitor workloads in a Teradata or multi-system environment (e.g.,within a domain 1200).

The Open APIs are written as scalar and table user defined functions(UDFs) and external stored procedures (XSPs). Scalar functions are usedfor interfaces that return a result. Table functions are used for theinterfaces that return data. External stored procedures are used forfunctions that write to a database system 100.

Open APIs have table and scalar user defined functions that interface toa database management system subsystem called a PerformanceMonitor/Application Programming Interface (PM/API). The PM/API subsystemprocesses requests from the open APIs. The open APIs can be usedactivate inactive categories, get delayed query lists, collect summarydata and statistics, and create “dynamic” rules. The PM/API interfacesare available through a log-on partition referred to herein as a VirtualMonitor using a specialized PM/API subset of the Call-Level Interfaceversion 2 (CLIv2). Further information regarding CLIv2 can be found inthe following references which are incorporated by reference herein:Teradata Call-Level Interface Version 2 Reference for Channel-AttachedSystems, NCR (Release Jun. 13, 2000, September 2006), and TeradataCall-Level Interface Version 2 Reference for Network-Attached Systems(Release Apr. 08, 2002, September 2006).

FIG. 14 illustrates how the Open API functions interface with thedatabase system 100 to retrieve the request data or perform therequested operation. As illustrated, the Open API functions 1400 (e.g.,XSPs or UDFs) access/logon to the virtual monitor partition 1402 toaccess PM/API interfaces. The PM/API interfaces retrieve the data fromthe DBS 100 into the virtual memory partition 1402. The data retrievedfrom the database 100 is found in the separate segmented memorypartitions of the global partition 1404. Examples of the segmentedglobal partitions 1404 include a session control global partition,dispatcher global partition, parser global partition, and the serviceconsole global partition. Each of such global partitions 1404 arepartitioned and segmented from each other and consist of various tasks.The virtual monitor partition 1402 allows access to all of thepartitions (including a partition's tasks) using SQL though a table thatis defined via the XSP/UDF 1400. Such a table is likely spooled tomemory and is not persistent. Alternatively, the table can also bestored in non-volatile memory.

Accordingly, the generic flow of each of the Open API functions 1400consists of an initial logon to the virtual monitor partition 1402. APMPC request by the open API function 1400 and the PM/API interfaces issent to the DBS 100 which fetches the appropriate response parcel fromthe specified global partition 1404. The response parcel is returned tothe calling Open API function 1400. If the PMPC request is to record aparcel, the value of the function return parameters may be set. The OpenAPI function 1400 then closes, finishes, and logs off of the virtualmonitor partition 1402.

In addition to the above, the XSP 1400 can accept an “XML” (extensiblemarkup language) file that defines the fields that should be selectedfrom global partition 1404. FIG. 15 illustrates the design of such aprocedure. As illustrated, the Open API function 1400 retrieves andparses the XML file at 1502. Once parsed, the fields are then used bythe Open API function 1400 to allow access to the globalpartitions/memory segments 1404 at 1504.

Thus, the Open APIs 1400 provide interfaces to obtain information anddisplay rows from global partitions 1404. The APIs 1400 can be writtenas XSP and UDF table functions. When UDF Table functions are beingutilized, the memory segment information can be accessed as though it isbeing selected from a virtual table (no persistent storage).Alternatively, the information can also be stored persistently in anactual table.

FIG. 16 illustrates the design for utilizing the Open APIs 1400 inaccordance with one or more embodiments of the invention. Asillustrated, the Open APIs 1400 access the global partitions 1404 (e.g.,via the virtual monitor partition 1402) and utilize the informationretrieved to display rows in a virtual monitor table 1602. Such avirtual monitor table can be queried or accessed using SQL.

An example of the use of Open APIs 1400 is useful to better understandthe invention. As described above, AMP Worker Tasks (AWTs) areconsidered a critical database resource. AWTs are threads/tasks that runinside of each virtual processor 110. These tasks perform the actualdatabase transactions. This database work may be triggered by theinternal database software routines, such as deadlock detection, or itmay be work originating from a user-submitted query. These pre-allocatedworker tasks are logically partitioned and assigned to a virtualprocessor (v-processor).

This logical partitioning requires that v-processors communicate withother v-processors through inter and intra-process communication (e.g.,via internal messages on a “BYNET”—Banyan Topology Network). Generallyspeaking, AWTs can work on a queue (well-known mailbox) queued up forwork, the AWT waits for work to arrive, performs the work, and returnsfor more work.

Due to their stateless condition, AWTs respond quickly to a variety ofdatabase execution needs. Certain databases (e.g., a Teradata™ database)may be designed to be a black box, with limited tuning knobs oraccessible parameters. Such databases provide that options and choicesare kept to a minimum and are designed to be self-managing. AWTs are oneof several virtual resources that can support parallel performance andshared-nothing architecture within a database system 100. AWTs are of afinite number, with a limited number available to perform new work onthe system 100.

AWTs are execution threads that do the work of executing a query step,once the step is dispatched to the AMP. AWTs also pick up the work ofspawned processes, and of internal tasks such as error logging oraborts. Not being tied to a particular session or transaction, AWTs areanonymous and immediately reusable and are able to take advantage of anyof the CPUs. Both AMPs and AWTs have equal access to any CPU on the node105.

A fixed number of AWTs are pre-allocated at startup for each AMP in theconfiguration, with the default number being 80. All of the allocatedAWTs can be active at the same time, sharing the CPUs and memory on thenode 105.

The solution to the problem described above (i.e., dynamic access tocritical system resources) is to enable the ability to dynamicallymonitor (i.e., via regulator 415 and system monitor 635) theavailability of these limited resources (i.e., AWTs) prior to allowingthe submission of queries. The regulator 415 can continuously monitorthe number of available AWTs of all the AMPs in the system 100 using theOpen APIs 1400. In addition to monitoring the number of AWTs, theregulator 415 can also monitor the number of messages pending on eachAMP (i.e., the message queue depth). Each message can represent the needfor another AWT. When the depth of the message queue reaches apredefined depth, that AMP will enter “flow control” and be preventedfrom accepting new work messages. With information on the number of AWTsand the number of messages pending to each AMP, dynamic decisions can bemade on allowing new queries to be submitted or delayed. In addition,the database administrator (DBA) 405 can predefine controls into theworkload rules 409. When a rule 409 is satisfied, the regulator 415 candynamically adjust the throttle count that controls the number ofqueries that can be allowed into the system 100. By preventing the AMPsfrom running out of AWTs, the regulator 415 can ensure a more continuousflow of work through the system 100.

In conclusion, while specific embodiments of a broader invention havebeen described herein, the present invention may also be carried out ina variety of alternative embodiments and thus is not limited to thosedescribed here. For example, while the invention has been described herein terms of a DBS that uses a massively parallel processing (MPP)architecture, other types of database systems, including those that usea symmetric multiprocessing (SMP) architecture, are also useful incarrying out the invention. Many other embodiments are also within thescope of the following claims.

1. A computer-implemented apparatus for managing a plurality of databasesystems, comprising: a domain comprised of a plurality of databasesystems, wherein a database in the plurality of database systems hassegmented global memory partitions; a virtual monitor partitionconfigured to provide logon access to the segmented global memorypartitions in a form of a virtual database; one or more open applicationprogramming interfaces (API) configured to logon to the virtual monitorpartition to access data in the virtual database; and a multi-systemregulator for managing the domain, wherein the multi-system regulator isconfigured to utilize the one or more open APIs to access data in thevirtual database.
 2. The apparatus of claim 1, wherein one of the openAPIs is written as an external stored procedure (XSP).
 3. The apparatusof claim 2, wherein the XSP accepts an extensible markup language (XML)file that defines fields to be selected from the segmented global memorypartitions.
 4. The apparatus of claim 1, wherein one of the open APIs iswritten as a user defined function (UDF).
 5. The apparatus of claim 4,wherein the UDF accesses the information from the segmented globalmemory partition as though selecting from a virtual table.
 6. Theapparatus of claim 1, wherein the open APIs logon to the virtual monitorpartition using a command line interface.
 7. A computer-implementedmethod for managing a plurality of database systems, comprising:obtaining a domain comprised of a plurality of database systems, whereina database in the plurality of database systems has segmented globalmemory partitions; logging onto, using one or more open applicationprogramming interfaces (API), a virtual monitor partition that isconfigured to provide logon access to the segmented global memorypartition in a form of a virtual database; and accessing data in thevirtual database using the open APIs and virtual monitor partition. 8.The method of claim 7, wherein one of the open APIs is written as anexternal stored procedure (XSP).
 9. The method of claim 8, furthercomprising the XSP accepting an extensible markup language (XML) filethat defines fields to be selected from the segmented global memorypartitions.
 10. The method of claim 7, wherein one of the open APIs iswritten as a user defined function (UDF).
 11. The method of claim 10,further comprising the UDF accessing the information from the segmentedglobal memory partition as though selecting from a virtual table. 12.The method of claim 7, wherein the open APIs logon to the virtualmonitor partition using a command line interface.
 13. An article ofmanufacture comprising one or more storage devices tangibly embodyinginstructions that, when executed by one or more computer systems, resultin the computer systems performing a method for managing a plurality ofdatabase systems, the method comprising: obtaining a domain comprised ofa plurality of database systems, wherein a database in the plurality ofdatabase systems has segmented global memory partitions; logging onto,using one or more open application programming interfaces (API), avirtual monitor partition that is configured to provide logon access tothe segmented global memory partition in a form of a virtual database;and accessing data in the virtual database using the open APIs andvirtual monitor partition.
 14. The article of manufacture of claim 13,wherein one of the open APIs is written as an external stored procedure(XSP).
 15. The article of manufacture of claim 14, the method furthercomprising the XSP accepting an extensible markup language (XML) filethat defines fields to be selected from the segmented global memorypartitions.
 16. The article of manufacture of claim 13, wherein one ofthe open APIs is written as a user defined function (UDF).
 17. Thearticle of manufacture of claim 16, the method further comprising theUDF accessing the information from the segmented global memory partitionas though selecting from a virtual table.
 18. The article of manufactureof claim 13, wherein the open APIs logon to the virtual monitorpartition using a command line interface.